earticle

영어

High-quality annotations are crucial for accurate object detection, but widely used datasets like MS-COCO face issues such as missing objects, duplicate labels, and inaccurate bounding boxes. To overcome these problems, MJ-COCO was created through model-driven refinement, increasing annotations from 860,001 to 1,221,970 instances. This paper presents a comparative analysis of MS-COCO and MJCOCO, with a focus on the accuracy of bounding box measurements. We designed a human-in-the-loop evaluation framework with custom software that enables side-by-side visualization of annotations, allowing evaluators to classify outcomes as improved, worse, or ambiguous. We collectively evaluated 41,754 annotations through a human-in-the-loop verification process involving fifteen human evaluators. The results demonstrate that a total of 25,754 annotations were improved, 2,398 were worsened, and 13,623 were ambiguous, for a total quality score of 89.49%. These findings show that MJ-COCO considerably enhances annotation quality and precision over MS-COCO, making it a more consistent and accurate standard for advancing object detection studies. The dataset and software codes are publicly available on Kaggle: https://www.kaggle.com/datasets/mjcoco2025/mj-coco-2025.